RNA_Content_Analysis_SPARQ_12122016
nathanbrandt
2017-01-03
Experiment overview
Write a detailed description of your experiment here including the goal of the analysis and your interpretation of the results.
If you still see this text it means that you have not described the experiment and whatever follows is meaningless. ###############################
This code is designed for use with the Accuri flow cytometer, which is equiped with the following lasers and filters
- Blue laser (488 nm)
- FL1 filter = 514/20nm GFP
FL3 filter = 575/25nm YFP
- Yellow/green laser (552 nm)
- FL2 filter = 610/20nm mCherry, dtomato
FL4 filter = 586/15nm DsRed
Requirements
In order to run this code you need:
+ to predefine your gates using the gating.R script
+ the gates.Rdata workspace, which contains the gates used in this script
+ the path of the directory(ies), given the variable names dir1, dir2… that contain .fcs files named A01.fcs, A02.fcs, A03.fcs…
+ a tab delimited sample sheet in each directory with the following rows:
+ the variable names are changed in chunk 2 named “Variable Names”
Output
This script generates a summary of results followed by quality control plots.
1 Step 1: Load relevant libraries
# This is a function that just makes sure you have a package, or installs it for you without prompting
requireInstall <- function(packageName,isBioconductor=F) {
if ( !try(require(packageName,character.only=T)) ) {
print(paste0("You don't have ",packageName," accessible, ",
"I'm gonna install it"))
if (isBioconductor) {
source("http://bioconductor.org/biocLite.R")
biocLite(packageName)
} else {
install.packages("packageName", repos = "http://cran.us.r-project.org")
}
}
return(1)
}
#Load libraries
requireInstall("flowCore",isBioconductor=T)## Loading required package: flowCore## [1] 1requireInstall("flowViz",isBioconductor=T)## Loading required package: flowViz## Loading required package: lattice## [1] 1requireInstall("flowStats")## Loading required package: flowStats## Loading required package: fda## Loading required package: splines## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:flowCore':
##
## %&%##
## Attaching package: 'fda'## The following object is masked from 'package:graphics':
##
## matplot## Loading required package: cluster## Loading required package: flowWorkspace## Loading required package: ncdfFlow## Loading required package: RcppArmadillo## Loading required package: BH## Loading required package: gridExtra## [1] 1requireInstall("Hmisc")## Loading required package: Hmisc## Loading required package: survival## Loading required package: Formula## Loading required package: ggplot2##
## Attaching package: 'Hmisc'## The following object is masked from 'package:gridExtra':
##
## combine## The following objects are masked from 'package:base':
##
## format.pval, round.POSIXt, trunc.POSIXt, units## [1] 1requireInstall("reshape2")## Loading required package: reshape2## [1] 1requireInstall("ggplot2")## [1] 1requireInstall("flowWorkspace")## [1] 1requireInstall("ggcyto", isBioconductor=T)## Loading required package: ggcyto## [1] 1requireInstall("gridExtra")## [1] 12 Step 2: Read in .fcs files, an Rdata file containing the gates sample sheet(s) that contains four columns with
- column1 = Well
- column2 = Strain
- column3 = Staining
- column4 = Media
- column5 = Userdefined
#Read in all data for analysis. Data should be in individual directories that contain .fcs files and a corresponding sample sheet with a generic format. FCS file names should be unaltered e.g AO1.fcs, A02.fcs, ...H12.fcs
#An abitrary number of directories can be used named dir1, dir2, dir3...with a corresponding flowData.1, flowData.2, flowData.3...and sample.sheet.1, sample.sheet.2, sample.sheet.3...
#load the Rdata file containing the gates
#load("gates.Rdata")
#Define the directory, or directories, containing your .fcs files using absolute path names
dir1 <- "/Users/nathanbrandt/Google Drive/Transfer_Folder/RNA_Content_Analysis_SPARQ/TotalRNA_KOMutants_12302016"
#Read in all the fcs files in the directory, with alter.names changing "-" to "."
flowData <- read.flowSet(path = dir1, pattern=".fcs", alter.names=TRUE)
#Read in the sample sheet that should be in each directory that contains the .fcs files.
sample.sheet <- read.csv("/Users/nathanbrandt/Google Drive/Transfer_Folder/RNA_Content_Analysis_SPARQ/TotalRNA_KOMutatnts_SampleSheet_12302016.csv")
#Change names of samples to those specified in the sample sheets
sampleNames(flowData) <- paste(sample.sheet[,5], sample.sheet[,1], sep=" ")#Check how many cells were counted in each fcs file
fsApply(flowData, each_col, length)[1:6]## [1] 10000 10000 10000 10000 10000 10000total <- fsApply(flowData, each_col, length)[1:6] #total counts per sample
#Print the medians of data values for each measurement
fsApply(flowData, each_col, median)## FSC.A SSC.A FL1.A FL2.A FL3.A FL4.A FSC.H SSC.H
## YPD A08 742810.0 105631.0 2336.0 290.5 720.0 296.0 1237046 161764.0
## YPD A09 1346577.0 262656.5 4291.0 472.0 1370.0 423.0 1844278 361436.5
## YPD A10 1265609.0 178553.5 5489.0 513.0 1602.0 452.0 1789920 241586.0
## YPD A11 986526.5 139176.5 3904.0 421.0 1180.0 367.0 1446638 192968.0
## YPD A12 862029.5 142669.5 4144.0 458.0 1182.0 534.0 1356770 210939.0
## YPD B08 757199.0 110522.0 2402.0 306.0 776.0 308.0 1210579 166025.5
## YPD B09 1223998.5 219028.5 3855.5 446.0 1239.0 410.0 1791315 319265.0
## YPD B10 1008374.5 147881.0 4583.0 418.0 1229.0 360.0 1536014 215950.0
## YPD B11 911052.5 147237.0 4041.0 437.0 1259.0 394.0 1390020 210051.5
## YPD B12 811110.5 133834.5 3843.0 436.0 1107.0 513.0 1320260 201250.0
## YPD C08 681743.5 106090.0 2229.0 302.0 753.0 318.0 1141735 164441.0
## YPD C09 1112718.5 188733.5 3467.0 440.0 1229.0 398.0 1637549 265234.5
## YPD C10 1229234.0 178298.0 5503.0 515.0 1539.0 472.0 1690244 232224.0
## YPD C11 972518.0 138999.0 3921.0 416.0 1203.0 372.0 1430436 190618.0
## YPD C12 761208.0 124384.5 3551.0 419.0 1081.0 502.0 1263607 189802.0
## YPD D08 695967.0 104745.5 11806.0 639.0 2407.5 580.0 1152895 160978.5
## YPD D09 1232015.5 255679.5 13844.0 808.0 2917.0 703.0 1786590 377614.5
## YPD D10 1308494.5 203013.0 38815.5 1691.0 7173.0 1420.0 1825238 271865.5
## YPD D11 901397.5 140235.5 24514.0 1165.0 4817.5 970.0 1411036 206119.5
## YPD D12 729496.5 123305.0 16085.0 886.0 3324.0 886.0 1205606 188380.5
## YPD E08 653503.0 99592.0 11125.0 611.0 2285.5 570.0 1123386 158140.5
## YPD E09 1150216.0 213446.5 15480.5 841.0 3167.0 738.0 1698964 310058.0
## YPD E10 1044756.5 170410.0 24621.0 1133.0 4672.0 961.0 1609627 249159.5
## YPD E11 966482.0 157241.0 31002.0 1421.0 6044.0 1221.0 1453502 218265.0
## YPD E12 712912.5 121074.5 18361.5 973.0 3755.5 944.0 1197579 189344.5
## YPD F08 702450.5 111375.5 12201.5 661.0 2530.0 602.0 1200076 176609.0
## YPD F09 1062344.5 184942.5 20780.0 1035.0 4148.5 906.0 1563341 256762.5
## YPD F10 1275642.5 197098.5 38608.0 1651.0 7060.5 1404.5 1749370 257572.0
## YPD F11 840404.5 134992.0 26892.5 1256.0 5309.0 1056.0 1362394 202327.5
## YPD F12 760096.0 122086.5 19194.0 1013.0 3840.0 999.0 1225548 183324.5
## FL1.H FL2.H FL3.H FL4.H Width Time
## YPD A08 2880.0 196.0 1520.0 426.0 58 173
## YPD A09 4316.0 306.0 1834.0 507.0 71 211
## YPD A10 5432.5 341.0 2024.0 519.0 71 241
## YPD A11 3986.0 276.0 1792.5 466.0 66 246
## YPD A12 4482.0 305.0 1775.0 556.0 61 200
## YPD B08 2847.0 204.0 1530.0 434.0 60 203
## YPD B09 4102.5 296.0 1781.5 495.0 67 201
## YPD B10 4888.5 289.0 1851.5 468.0 65 215
## YPD B11 4228.0 293.0 1816.5 479.0 63 210
## YPD B12 4242.5 291.0 1751.0 550.0 60 200
## YPD C08 2726.5 203.0 1524.0 434.0 57 193
## YPD C09 3716.0 284.0 1774.0 492.0 67 207
## YPD C10 5244.0 332.0 1988.5 522.0 71 268
## YPD C11 4008.0 275.0 1794.0 476.0 66 247
## YPD C12 4047.0 284.0 1728.0 543.0 59 202
## YPD D08 13032.0 529.0 2709.0 609.0 58 197
## YPD D09 13233.5 616.0 2946.5 683.0 68 201
## YPD D10 35488.0 1395.5 6531.0 1240.0 70 232
## YPD D11 24898.0 1011.0 4783.0 929.0 62 214
## YPD D12 17079.0 728.0 3444.0 819.5 58 204
## YPD E08 12689.0 517.0 2625.0 603.0 56 195
## YPD E09 14740.5 656.0 3160.0 708.0 67 200
## YPD E10 24905.0 974.0 4671.0 911.0 65 201
## YPD E11 29875.0 1207.5 5681.0 1095.0 66 225
## YPD E12 19905.5 840.0 3967.0 879.5 57 199
## YPD F08 13750.0 565.0 2843.0 630.0 56 194
## YPD F09 19917.0 824.0 3899.0 825.0 66 209
## YPD F10 34845.0 1318.0 6197.5 1199.0 72 262
## YPD F11 27973.0 1131.5 5353.0 1015.0 61 198
## YPD F12 19965.0 836.0 3918.5 905.0 60 205samples.num <- length(flowData) #number of samples3 Step 3: apply filters to data and generate plots showing the effect on filtering
##Subset the data by applying sequential gates##
#apply doublet gate
flowData.singlets <- Subset(flowData, pg.singlets)
fsApply(flowData.singlets, each_col, length)[1:samples.num]
singlets <- fsApply(flowData.singlets, each_col, length)[1:samples.num]
barplot(singlets/total, ylim=c(0,1), ylab = "Proportion singlet cells", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#apply debris gate
filteredData <- Subset(flowData.singlets, pg.nondebris)
fsApply(filteredData, each_col, length)[1:samples.num]
non.debris <- fsApply(filteredData, each_col, length)[1:samples.num]
barplot(non.debris/total, ylim=c(0,1), ylab = "Proportion singlet and nondebris cells", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#########
#filteredData is the variable name for the data filtered of doublets and debris that are used for all subsequent analyses
##########
#this gate defines nongfp cells
gfp.neg <- Subset(filteredData, pg.nongfp)
fsApply(gfp.neg, each_col, length)[1:samples.num]
non.gfp <- fsApply(gfp.neg, each_col, length)[1:samples.num]
barplot(non.gfp/non.debris, ylim=c(0,1), ylab = "Proportion cells with no GFP", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#this gate defines gfp cells
gfp.pos <- Subset(filteredData, pg.gfp)
fsApply(gfp.pos, each_col, length)[1:samples.num]
gfp.cells <- fsApply(gfp.pos, each_col, length)[1:samples.num]
barplot(gfp.cells/non.debris, ylim=c(0,1), ylab = "Proportion cells with GFP", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#this gate defines high GFP cells
gfp.hi <- Subset(filteredData, pg.hi.gfp)
fsApply(gfp.hi, each_col, length)[1:samples.num]
hi.gfp.cells <- fsApply(gfp.hi, each_col, length)[1:samples.num]
barplot(hi.gfp.cells/non.debris, ylim=c(0,1), ylab = "Proportion cells with high GFP", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))filteredData <- flowData4 Step 4: Data analysis
4.1 diagnostic values can be defined for plotting purposes
#define critical values that can superimposed on plots for easy visual comparison
gfp.bg <- 3.9 #a background value for GFP
gfp.wt <- 5.9 #a value for wildtype GFP expression
red.bg <- 3.03 #a background value for the red channel
red.wt <- 3.75 #a value for wildtype Red expression
haploid.fsc <- 6e5 #an empirical value for forward scatter for haploids
diploid.fsc <- 7e5 #an empirical value for forward scatter for diploids
gfp.norm <- 0.935 #an empricial value for gfp expression normalized by forward scatter
red.norm <- 0.57 #an empricial value for red expression normalized by forward scatter
gfp.red.norm <- 1.5 #an empricial value for gfp expression normalized by red channel4.2 Extract data from fcs files to generate statistics and boxplots
#record summary statistics for each sample in a matrix named summary.stats
summary.stats <- matrix(data = NA, nrow = length(filteredData), ncol = 18, dimnames = list(sampleNames(filteredData),c("FSC_median","FSC_mean", "FSC_sd","FL1_median", "FL1_mean","FL1_sd","normalizedGFP_median", "normalizedGFP_mean", "normalizedGFP_sd","FL2_median","FL2_mean","FL2_sd","normalizedRed_median","normalizedRed_mean", "normalizedRed_sd","GFPnormalizedByRed_median", "GFPnormalizedByRed_mean","GFPnormalizedByRed_sd")))
#use the sample containing the minimum number of points after filtering for doublets and debris to define the number of data points retained for all samples
sample.size <- min(fsApply(filteredData, each_col, length))
print(sample.size)## [1] 10000comparison.FSC <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL1 <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL2 <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL1NormFsc <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL2NormFsc <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL1NormFL2 <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
#for each sample plot a histogram of the normalized data, raw FSC and raw GFP per row
par(mfrow=c(1,2), mar=c(5.1,2.1,2.1,2.1), oma=c(1.5,2,1,1))
#extract data from flowFrames to plot histograms of values and record summary statistics
for (i in 1:length(filteredData)){
temp <- exprs(filteredData[[i]]) #exprs() extracts a matrix of the values from the flowframe
##########################################
#record summary statistics for the sample#
##########################################
#FSC
summary.stats[i,1] <- median(temp[,1])
summary.stats[i,2] <-mean(temp[,1])
summary.stats[i,3] <- sd(temp[,1])
#FL1
summary.stats[i,4] <- median(temp[,3])
summary.stats[i,5] <-mean(temp[,3])
summary.stats[i,6] <- sd(temp[,3])
#FL1 (GFP) divided by FSC
summary.stats[i,7] <- median(temp[,3]/temp[,1])
summary.stats[i,8] <-mean(temp[,3]/temp[,1])
summary.stats[i,9] <- sd(temp[,3]/temp[,1])
#FL2
summary.stats[i,10] <- median(temp[,4])
summary.stats[i,11] <-mean(temp[,4])
summary.stats[i,12] <- sd(temp[,4])
#FL2 (Red) divided by FSC
summary.stats[i,13] <- median(temp[,4]/temp[,1])
summary.stats[i,14] <-mean(temp[,4]/temp[,1])
summary.stats[i,15] <- sd(temp[,4]/temp[,1])
#FL1 (GFP) divided by FL2 (Red)
summary.stats[i,16] <- median(temp[,3]/temp[,4])
summary.stats[i,17] <-mean(temp[,3]/temp[,4])
summary.stats[i,18] <- sd(temp[,3]/temp[,4])
##############################################
#plot histograms of the channels of interest##
##############################################
###############
#Green channel#
###############
#FL1 (GFP)
hist(log10(temp[,3]), br=1000, xlab = "log10(FL1)", main = "FL1")
abline(v=gfp.bg, col="yellow", lty=2, lwd=2)
abline(v=gfp.wt, col="green", lty=2, lwd=2)
legend("topleft", legend=paste("median FL1 = ",round(median(temp[,3]), digits=4),sep=""))
#GFP divided by FSC
hist(temp[,3]/temp[,1], br=500, xlab = "FL1/FSC", main = "FL1/FSC")
abline(v=gfp.norm, col="green", lty=2, lwd=2 )
legend("topleft", legend=paste("median GFP / FSC=",round(median(temp[,3]/temp[,1]), digits=4),sep=""))
mtext(sampleNames(filteredData[i]), outer = TRUE, cex = 1.0)
###############
#Red channel#
###############
#FL2 (Red)
hist(log10(temp[,4]), br=500, xlab = "log10(FL2)", main = "FL2")
abline(v=red.bg, col="yellow", lty=2, lwd=2)
abline(v=red.wt, col="red", lty=2, lwd=2)
legend("topleft", legend=paste("median FL2=",round(median(temp[,4]), digits=4),sep=""))
#FL2 divided by FSC
hist(temp[,4]/temp[,1], br=500, xlab = "FL2/FSC", main = "FL2/FSC")
abline(v=red.norm, col="red", lty=2, lwd=2 )
legend("topleft", legend=paste("median FL2 / FSC=",round(median(temp[,4]/temp[,1]), digits=4),sep=""))
mtext(sampleNames(filteredData[i]), outer = TRUE, cex = 1.0)
###############
#Other#########
###############
#FL1 divided by FL2
hist(temp[,4]/temp[,3], br=500, xlab = "FL2/FL1", main = "FL1/FL2")
abline(v=gfp.red.norm, col="purple", lty=2, lwd=2)
legend("topleft", legend=paste("median FL1 / FL2=",round(median(temp[,4]/temp[,3]), digits=4),sep=""))
#FSC
hist(log10(temp[,1]), br=500, xlab = "log10(FSC)", main = "FSC", xlim=c(4,8))
abline(v=haploid.fsc, col="blue", lty=2, lwd=2)
abline(v=diploid.fsc, col="grey", lty=2, lwd=2)
legend("topleft", legend=paste("median FSC=",round(median(temp[,1]), digits=4),sep=""))
mtext(sampleNames(filteredData[i]), outer = TRUE, cex = 1.0)
print("-------------------------------------------------------")
print("-----------------------------------")
print("----------------------")
############################################################
#keep the data set for generating boxplots comparing values#
############################################################
#Note that the amount of data kept for each sample is defined by the lowest count among all the samples.
comparison.FSC[1:sample.size,i] <- temp[1:sample.size,1] #FSC
comparison.FL1[1:sample.size,i] <- temp[1:sample.size,3] #FL1 (GFP)
comparison.FL1NormFsc[1:sample.size,i] <- temp[1:sample.size,3]/temp[1:sample.size,1] #GFP/FSC
comparison.FL2[1:sample.size,i] <- temp[1:sample.size,4] #FL2
comparison.FL2NormFsc[1:sample.size,i] <- temp[1:sample.size,4]/temp[1:sample.size,1] #FL2/FSC
comparison.FL1NormFL2[1:sample.size,i] <- temp[1:sample.size,3]/temp[1:sample.size,4] #FL1/FL2
}
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## [1] "----------------------"par(mfrow=c(1,1)) #change number of plots per row back to standard4.3 Overview of data distributions
par(mar=c(8.1,4.1,4.1,2.1)) #create more space at lower margin
boxplot(comparison.FSC, names=sampleNames(filteredData), notch = TRUE, col = "gray", ylab="FSC", cex.axis=0.5,las=2, outline=F)
abline(h=haploid.fsc, lty=2, col=2)
abline(h=diploid.fsc, lty=2, col=3)
boxplot(comparison.FL1, names=sampleNames(filteredData), notch = TRUE, col = "lightgreen", ylab="FL1", cex.axis=0.5,las=2, outline=F)
abline(h=gfp.bg ,lty=2, lwd=3, col="yellow")
abline(h=gfp.wt, lty = 2, lwd=3, col="green")
boxplot(comparison.FL1NormFsc, names=sampleNames(filteredData), notch = TRUE, col = "green", ylab="FL1/FSC", cex.axis=0.5,las=2, outline=F)
abline(h=gfp.norm, lty=2, lwd=3, col="blue")
boxplot(comparison.FL2, names=sampleNames(filteredData), notch = TRUE, col = "pink", ylab="FL2", cex.axis=0.5,las=2, outline=F)
abline(h=red.bg, lty=2, lwd=3, col="pink")
abline(h=red.wt, lty=2, lwd=3, col="red")
boxplot(comparison.FL2NormFsc, names=sampleNames(filteredData), notch = TRUE, col = "red", ylab="FL2/FSC", cex.axis=0.5,las=2, outline=F)
abline(h=red.norm, lty=2, lwd=3, col="red")
boxplot(comparison.FL1NormFL2, names=sampleNames(filteredData), notch = TRUE, col = "purple", ylab="FL1/FL2", cex.axis=0.5,las=2, outline=F)
abline(h=gfp.red.norm, lty=2, lwd=3, col="purple")
par(mar=c(5.1,4.1,4.1,2.1)) #reset margins to default
#generate a summary table containing all the recorded statistics
print(summary.stats)## FSC_median FSC_mean FSC_sd FL1_median FL1_mean FL1_sd
## YPD A08 742810.0 792708.3 329268.2 2336.0 2614.984 1842.918
## YPD A09 1346577.0 1657133.4 985547.1 4291.0 5931.889 6139.960
## YPD A10 1265609.0 1434726.6 721463.9 5489.0 7357.011 13211.111
## YPD A11 986526.5 1066982.5 433697.9 3904.0 4484.376 3912.956
## YPD A12 862029.5 1003578.4 536094.3 4144.0 5328.145 22559.867
## YPD B08 757199.0 789459.6 292469.2 2402.0 2640.585 1864.891
## YPD B09 1223998.5 1483578.3 854039.6 3855.5 5188.974 5382.356
## YPD B10 1008374.5 1104004.4 451416.8 4583.0 5749.260 24698.509
## YPD B11 911052.5 1023866.3 442558.2 4041.0 4660.298 3173.095
## YPD B12 811110.5 941416.7 482495.6 3843.0 4672.877 5956.824
## YPD C08 681743.5 710805.9 260624.2 2229.0 2438.010 1676.600
## YPD C09 1112718.5 1368676.4 935275.4 3467.0 4991.442 18937.409
## YPD C10 1229234.0 1431410.0 830975.0 5503.0 7520.966 11557.942
## YPD C11 972518.0 1044102.3 381634.9 3921.0 4366.130 3123.697
## YPD C12 761208.0 872729.3 444241.7 3551.0 4278.887 4593.657
## YPD D08 695967.0 733236.1 281766.1 11806.0 16715.858 60431.555
## YPD D09 1232015.5 1553220.0 988601.3 13844.0 64289.306 384723.991
## YPD D10 1308494.5 1557521.2 920223.6 38815.5 74676.000 264238.784
## YPD D11 901397.5 1015753.4 479110.5 24514.0 61809.291 243183.799
## YPD D12 729496.5 847154.8 446357.3 16085.0 29683.438 104053.885
## YPD E08 653503.0 683548.1 246869.5 11125.0 15380.710 55242.586
## YPD E09 1150216.0 1414348.4 852553.6 15480.5 69196.059 392581.520
## YPD E10 1044756.5 1238610.5 645132.6 24621.0 44621.607 154928.528
## YPD E11 966482.0 1106789.0 513260.9 31002.0 58537.874 179444.190
## YPD E12 712912.5 825251.4 440900.9 18361.5 28280.613 108923.016
## YPD F08 702450.5 746631.3 302076.0 12201.5 16116.922 51131.416
## YPD F09 1062344.5 1306632.7 883230.9 20780.0 62836.646 335407.993
## YPD F10 1275642.5 1497973.8 998236.3 38608.0 76301.407 249004.002
## YPD F11 840404.5 944568.0 440851.8 26892.5 60374.443 201969.852
## YPD F12 760096.0 875804.9 455630.9 19194.0 29440.556 102214.752
## normalizedGFP_median normalizedGFP_mean normalizedGFP_sd
## YPD A08 0.003145762 0.003241004 0.002165258
## YPD A09 0.003000535 0.003586443 0.007426854
## YPD A10 0.004406697 0.005717447 0.055146370
## YPD A11 0.003909402 0.004240498 0.007471744
## YPD A12 0.004747088 0.005309516 0.033263467
## YPD B08 0.003204836 0.003397927 0.005972742
## YPD B09 0.003087417 0.003371367 0.009826078
## YPD B10 0.004573783 0.005372198 0.026304749
## YPD B11 0.004326694 0.004507579 0.002937904
## YPD B12 0.004646470 0.004758991 0.004341018
## YPD C08 0.003281002 0.003443081 0.002836580
## YPD C09 0.003064734 0.003640234 0.024384685
## YPD C10 0.004523261 0.005555245 0.027400172
## YPD C11 0.003961940 0.004244351 0.004755122
## YPD C12 0.004639377 0.004754842 0.004074160
## YPD D08 0.017006986 0.027982566 0.157385500
## YPD D09 0.010491852 0.038016083 0.218428381
## YPD D10 0.029790367 0.052950111 0.190931352
## YPD D11 0.026861892 0.062081508 0.250716366
## YPD D12 0.021882549 0.042411127 0.196247081
## YPD E08 0.017091500 0.026405849 0.153608694
## YPD E09 0.012823962 0.044726541 0.264179236
## YPD E10 0.023292599 0.037740692 0.153935995
## YPD E11 0.031725973 0.054065785 0.179395420
## YPD E12 0.025624258 0.041162741 0.179317128
## YPD F08 0.017319170 0.024028597 0.121435720
## YPD F09 0.019516915 0.048515258 0.234200909
## YPD F10 0.030699347 0.071218127 0.213620746
## YPD F11 0.031598070 0.079608699 0.322855029
## YPD F12 0.025013389 0.041580018 0.210750761
## FL2_median FL2_mean FL2_sd normalizedRed_median
## YPD A08 290.5 315.5469 252.2474 0.0003868290
## YPD A09 472.0 559.8288 513.6925 0.0003208470
## YPD A10 513.0 587.6794 518.4994 0.0003935869
## YPD A11 421.0 450.6628 328.0240 0.0004129327
## YPD A12 458.0 520.1475 957.4983 0.0005043213
## YPD B08 306.0 330.4406 252.4175 0.0004021681
## YPD B09 446.0 524.7946 495.6713 0.0003368130
## YPD B10 418.0 460.9348 473.3539 0.0004040948
## YPD B11 437.0 467.2998 316.4380 0.0004557287
## YPD B12 436.0 483.3892 363.8484 0.0005071488
## YPD C08 302.0 324.0561 250.1710 0.0004409346
## YPD C09 440.0 526.5799 620.5321 0.0003667084
## YPD C10 515.0 603.8036 559.4277 0.0004053670
## YPD C11 416.0 445.9411 313.2359 0.0004178547
## YPD C12 419.0 466.8401 337.9068 0.0005309621
## YPD D08 639.0 1358.8885 34061.2218 0.0009174652
## YPD D09 808.0 2979.4344 18472.2969 0.0006245038
## YPD D10 1691.0 3460.8502 19583.2648 0.0013012002
## YPD D11 1165.0 2987.5530 15228.1377 0.0012790067
## YPD D12 886.0 1771.9594 17448.1266 0.0011920138
## YPD E08 611.0 842.9336 5044.7999 0.0009401279
## YPD E09 841.0 3014.2809 16367.6497 0.0007122117
## YPD E10 1133.0 2011.2571 8524.7118 0.0010653347
## YPD E11 1421.0 2768.4319 11365.9532 0.0014557269
## YPD E12 973.0 1969.1786 13936.5339 0.0013324213
## YPD F08 661.0 915.3549 5222.9472 0.0009413378
## YPD F09 1035.0 3179.9038 19401.3737 0.0009693874
## YPD F10 1651.0 5449.4671 58343.8235 0.0013117202
## YPD F11 1256.0 3075.6721 34305.5307 0.0014624174
## YPD F12 1013.0 1857.6888 9701.9855 0.0013071705
## normalizedRed_mean normalizedRed_sd GFPnormalizedByRed_median
## YPD A08 0.0004318877 0.0004114477 NA
## YPD A09 0.0003641823 0.0005282887 NA
## YPD A10 0.0004585259 0.0008992686 NA
## YPD A11 0.0004546021 0.0004889242 NA
## YPD A12 0.0005604510 0.0013980886 NA
## YPD B08 0.0004522890 0.0004325093 NA
## YPD B09 0.0003699618 0.0007687724 NA
## YPD B10 0.0004500393 0.0005414024 NA
## YPD B11 0.0004761178 0.0003342927 NA
## YPD B12 0.0005379246 0.0004436269 NA
## YPD C08 0.0004958333 0.0005263794 NA
## YPD C09 0.0003994040 0.0004811809 NA
## YPD C10 0.0004587870 0.0007038300 NA
## YPD C11 0.0004605465 0.0004784886 NA
## YPD C12 0.0005767688 0.0004910103 NA
## YPD D08 0.0031253746 0.0634947448 18.93086
## YPD D09 0.0021427171 0.0255674518 NA
## YPD D10 0.0047131948 0.0639357745 23.45983
## YPD D11 0.0057623714 0.0871071206 21.62553
## YPD D12 0.0045287930 0.0646267501 18.93984
## YPD E08 0.0018303002 0.0307576249 18.69508
## YPD E09 0.0022010523 0.0173697001 NA
## YPD E10 0.0031594630 0.0514532594 22.66338
## YPD E11 0.0052925091 0.0761294315 22.20349
## YPD E12 0.0082346136 0.1020210935 NA
## YPD F08 0.0026250234 0.0414137528 NA
## YPD F09 0.0055697405 0.0727148720 NA
## YPD F10 0.0141066475 0.1417883903 NA
## YPD F11 0.0058573559 0.0808800117 21.98566
## YPD F12 0.0077849148 0.1118704018 19.66870
## GFPnormalizedByRed_mean GFPnormalizedByRed_sd
## YPD A08 NaN NA
## YPD A09 NaN NA
## YPD A10 NaN NA
## YPD A11 NaN NA
## YPD A12 NaN NA
## YPD B08 NaN NA
## YPD B09 NaN NA
## YPD B10 NaN NA
## YPD B11 NaN NA
## YPD B12 NaN NA
## YPD C08 NaN NA
## YPD C09 NaN NA
## YPD C10 NaN NA
## YPD C11 NaN NA
## YPD C12 NaN NA
## YPD D08 Inf NaN
## YPD D09 NaN NA
## YPD D10 Inf NaN
## YPD D11 Inf NaN
## YPD D12 Inf NaN
## YPD E08 Inf NaN
## YPD E09 NaN NA
## YPD E10 Inf NaN
## YPD E11 Inf NaN
## YPD E12 NaN NA
## YPD F08 NaN NA
## YPD F09 NaN NA
## YPD F10 NaN NA
## YPD F11 Inf NaN
## YPD F12 Inf NaNsummary.stats <- as.data.frame(summary.stats)4.4 Quantitation of relative FL1 signal
baseline.FL1 <- summary.stats$FL1_median[1]
barplot(summary.stats$FL1_median/baseline.FL1, ylab="Relative FL1 median expression", las=2, cex.names = 0.5, names.arg=sampleNames(filteredData))
4.5 Quantitation of forward scatter
baseline.FSC <- summary.stats$FSC_median[1]
barplot(summary.stats$FSC_median/baseline.FSC, ylab="Relative median FSC", las=2, cex.names = 0.5, names.arg=sampleNames(filteredData))
4.6 Population composition
pop.composition <- rbind(non.gfp/non.debris,gfp.cells/non.debris,hi.gfp.cells/non.debris)
barplot(pop.composition, ylab="Proportion of population", legend=c("No GFP", "Normal GFP", "High GFP"),las=2, cex.names = 0.5,names.arg=sampleNames(filteredData))5 Step 5: Quality control
5.1 Gates
###First flowset
#Singlets gate
xyplot(FSC.A~FSC.H, data=flowData.1, xlim=c(0,3e6), ylim=c(0,3e6), filter=pg.singlets, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - singlets gate")
#Debris gate
xyplot(SSC.A ~ FSC.A, data=flowData.1, displayFilter=TRUE, xlim=c(0,3e6), ylim=c(0,3e5), filter=pg.nondebris, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - nondebris gate")
#Non-fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.1, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.nongfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - non GFP gate")
#Fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.1, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - GFP gate")
#High fluorescing gate
xyplot(FL1.A~FSC.A,data=flowData.1, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.hi.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - high GFP gate")
################
###Second flowset
#Singlets gate
xyplot(FSC.A~FSC.H, data=flowData.2, xlim=c(0,3e6), ylim=c(0,3e6), filter=pg.singlets, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - singlets gate")
#Debris gate
xyplot(SSC.A ~ FSC.A, data=flowData.2, displayFilter=TRUE, xlim=c(0,3e6), ylim=c(0,3e5), filter=pg.nondebris, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - nondebris gate")
#Non-fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.2, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.nongfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - non GFP gate")
#Fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.2, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - GFP gate")
#High fluorescing gate
xyplot(FL1.A~FSC.A,data=flowData.2, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.hi.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - high GFP gate")
#####Attempted as loop below to plot each one individually and is not working
for (i in 1:length(filteredData)){
#Singlets gate
xyplot(FSC.A~FSC.H, data=flowData[i], xlim=c(0,3e6), ylim=c(0,3e6), filter=pg.singlets, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#Debris gate
xyplot(SSC.A ~ FSC.A, data=flowData[i], displayFilter=TRUE, xlim=c(0,3e5), ylim=c(0,3e6), filter=pg.nondebris, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#Non-fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData[i], displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.nongfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#Fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData[i], displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#High fluorescing gate
xyplot(FL1.A~FSC.A,data=flowData[i], xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.hi.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
}5.2 Data transformation for visualization
#In order to look at QC plots the data is transformed using the logicle transform, which is a log transform for high values that transitions to a linear transformation near zero values
#This is simply for visualization purposes
lgcl <- logicleTransform(w = 0.5, t= 10000, m=4.5) #the parameters w,t, and m define the transformation parameters
#Dataset 1 tranformation applied to every channel except width and time
dataLGCLTransform <- transform(filteredData,'FSC.A' = lgcl(`FSC.A`), 'SSC.A' =lgcl(`SSC.A`), 'FL1.A' = lgcl(`FL1.A`), 'FL2.A' = lgcl(`FL2.A`), 'FL3.A' = lgcl(`FL3.A`), 'FL4.A' = lgcl(`FL4.A`),'FSC.H' = lgcl(`FSC.H`),'SSC.H' = lgcl(`SSC.H`),'FL1.H' = lgcl(`FL1.H`),'FL2.H' = lgcl(`FL2.H`),'FL3.H' = lgcl(`FL3.H`),'FL4.H' = lgcl(`FL4.H`)) 5.3 Effect of time
#The effect of time on signal (of which there shouldn't be any)
i <- 1
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
i <- 2
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
i <- 3
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
####Attempted as loop and will not work
for (i in 1:length(filteredData)){
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
}5.4 Plots of FL1 versus FSC
i <- 1
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 2
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 3
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
####Attempted as loop and will not work
for (i in 1:length(filteredData)){
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(2,6), sampleNames(filteredData)[i])
}5.5 Plots of FSC versus SSC
i <- 1
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 2
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 3
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
####Attempted as loop and will not work
for (i in 1:length(filteredData)){
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])
}